The invention relates to an image processing apparatus outputting multigradation image data which has been converted into bigradation image data capable of displaying half tones. In particular it relates to improvement of an image processing apparatus using an error diffusion method or a minimum average error method.
In the past, multigradation image data read using an input from a scanner or from a computer was reproductively displayed, for example, by multigradation displays, printers, facsimiles and copies. Using these as image output apparatus to reproductively display multigradation image data posed no problem. However, in the case where a printer apparatus or a display apparatus which does not control the gradation of dot is used, it was necessary to perform binary coding processing to reduce the number of gradations of each pixel to 2 gradations.
Furthermore, in the case where the volume of the afore-mentioned multigradation image data was reduced in order to store or transfer it, binary coding processing was performed in the same way to reduce the number of gradations of each pixel to 2.
There are many types of techniques of binary coding. Among them, an error diffusion method and an equivalent minimum average error method are widely used as the to produce superior picture, quality. Art error diffusion method or a minimum average error method, having high resolution, has the superior characteristic of continuous gradation reproduction.
In the above-mentioned error diffusion method, a quantization error occurring during binary coding of a target is diffused and added to the proximal non binary-coded pixels. On the other hand, the minimum average error method is a method whereby the weighted average value of a quantization error occurring in previously binary-coded pixels amends the data value of the following target pixel. The error diffusion method and the minimum average error method are different only with respect to when the error diffusion operation is carried out. Logically they are the same. Unexamined Japanese Patent Publication No. Hei. 1-284173 is an example employing the error diffusion method.
However, in the previous image processing apparatus utilizing the error diffusion method and the minimum average error method, when converting multigradation image data to bigradation image data there were the following problems:
First, black dot generation in the initial part of an area of low density (an area with few black dots), and white dot generation in the initial part of an area of high density (an area with few white dots), was substantially delayed. As a result, in the worst case, the image was transformed.
Further, even after an area of low density and an area of high density were finished, there was the problem that a phenomenon (tailing) developed. In this case, the image data following the completion of an area of low density was deflected towards the high density side, and image data following the completion of an area of high density was deflected towards the low density side.
Consider the case of the first problem in a bigradational printer apparatus printing black ink dots on white paper. Because the black dots got bigger due to ink blurring, the white dots were difficult to distinguish because of blurring from the surrounding black dots. As a result, the first problem was especially noticeable in the low density portion.
The afore-mentioned problems will be discussed in detail using the drawings. FIG. 13 is an original image 100 displayed utilizing multiple gradations. Inside a high-density square area 110 with a density gradation value of 252 (highest value- 255), there is a low density square area 120 with a density gradation value of 3 (lowest density- 0).
Further, at the bottom right of the low density square area 120 a 45 degree inclined straight line with a density gradation value of 231 (slightly lower density than the 252 background density) is drawn.
In FIG. 14(A), in a previous technology utilizing an error diffusion method, the original image 100 (shown in FIG. 13.) multigradation image data binary-coded image is shown. Further, in FIG. 14(B), an outline diagram shows the position of 14(A). This binary-coded image was obtained by a repeated binary coding process, with the upper left-hand corner pixel of original image 100 as the binary coding starting point; after one line is binary coded by scanning to the right the coding moves to one pixel lower at the left-hand end and continues the process. In contrast to FIG. 14A, FIG. 12 is an example of the output in the case of ideal binary coding of the same original image 100 by the image processing apparatus of the present invention. If we compare the two, in FIG. 14 in the upper and left-hand portions of the high density square area 140, (Please refer to FIG. 14(B)) the generation of white dots was delayed. Further, in the upper and left-hand portions of the low density square area 120, delay in the generation of black dots occurred. That is, the above-mentioned first problem occurred in the portions 140 and 142. Further, due to the influence of tailing from the square-shaped low density area 120, one portion (132) of straight line 130 in the bottom right-hand area 150 disappeared. In this way, the second problem occurred in area 150.
Unexamined Japanese Patent Application No. Hei.1-130945 proposed a solution to these problems. In the first embodiment mentioned, in the case of managing from 0-255 input density data, where the input density data was between 1 and 29, the threshold value changed randomly. More specifically, where the input data was between 1-4, the threshold value was between 20-230, where the input data was between 5-14, the threshold value was between 50-200 and where the input data was between 15-29, the threshold value was between 100-150. This is the range within which the threshold value changed randomly. The random noise range was .+-.105 when the data was from 1-4, .+-.75 when the data was from 5-14 and .+-.25 when the data was from 15-29; the result was that a large noise was added in a low density area close to 0. However, the expected values of the threshold values were all uniformally at 125. In this way the delay in dot generation was improved, because the case developed wherein when the density was low the threshold value became exceptionally small due to a large threshold value noise, and even in the transitional portion of an area of low gradation density, pixels binary coded to 255 developed.
Therefore, in the case of improving the first problem by this method, because there was exceptionally high noise in the area of low density, a low quality image resulted. Further, in this previous example a special structure called a determining circuit was provided. Using this determining circuit, if upon examination of the binary coding result of binary-coded pixels surrounding the target pixel there were already dots adjacent, a determining process was employed to determine whether the target pixels were to be binary coded. It is considered that this improves the above-mentioned problems a little.
However, in the above process, it is necessary to refer to the binary coding result of the surrounding 12 pixels. Complicated processing became necessary, and there were the problems of increased processing time and unsatisfactory picture quality. In addition to this, in this related art the improvement in the second problem was insufficient.
Still further, in the other embodiments mentioned in this related art, there are discrepancies in the way of defining a signal 410. FIG. 1 of the publication, which shows the example, is therefore difficult to correctly understand. However, as it is stated in the related art, `it has a threshold value setting function in the same way as in the above-mentioned embodiment, moreover the scale of the hardware is small`, it is considered that, as in the first embodiment, a large volume of noise was added to the threshold value in the area of low density. Accordingly, the other embodiments mentioned in this related art had the same problems as the first embodiment.
Further, as other methods of improving the aforementioned first and second problems there are proposals such as the `Image Processing Apparatus` of Unexamined Japanese Patent Application No. Hei.3-112269, and the `Image Forming Apparatus` of Unexamined Japanese Patent Application No. Hei.4-126464, etc. In this related art, an average density value was inferred by referencing the result of binary coding of a plurality of pixels surrounding the target pixels, and binary coding of the target pixels was carried out with the average as a threshold value. However, in this method there were the following two problems:
(1) It was necessary to reference the result of binary coding of more than 10 surrounding pixels, so there was the problem that it took a long time for processing and a complicated processing circuit was necessary. PA1 (2) In the boundaries where the data changed suddenly, it was inappropriate to use an average density value from the surrounding pixels. The result was the problem of incorrect binary coding being performed and noise being created. PA1 in the case where the value of data is close to the first gradation value, data.ltoreq.slsh.ltoreq.(m+data)/2, and in the case where the value of data is close to the second gradation value, (m+data)/2.ltoreq.slsh.ltoreq.data. PA1 an error storage portion which stores each pixel error; PA1 a data correction portion which calculates said corrected pixel data by finding an average error by reading the binary coding error of the pixels proximate to the target pixels stored in said error storage portion and applying a predetermined weighting and adding this average error to the target pixel multigradation data; corrects target pixel multigradation image data using an minimum average error method and outputs corrected pixel data.